Online PHP Training #training trains

PHP Training

Introduction of Web & PHP

What is PHP?

The history of PHP

Why choose PHP?

Installation overview

First Steps

Embedding PHP code on a page

Outputting dynamic text

The operational trail

Inserting code comments

Exploring Data Types

Variables

Strings

String functions

Numbers part one: Integers

Numbers part two: Floating points

Arrays

Associative arrays

Array functions

Booleans

NULL and empty

Type juggling and casting

Constants

Control Structures: Logical Expressions

If statements

Else and elseif statements

Logical operators

Switch statements

Control Structures: Loops

While loops

For loops

Foreach loops

Continue

Break

Understanding array pointers

User-Defined Functions

Defining functions

Function arguments

Returning values from a function

Multiple return values

Scope and global variables

Setting default argument values

Debugging

Common problems

Warnings and errors

Debugging and troubleshooting

Building Web Pages with PHP

Links and URLs

Using GET values

Encoding GET values

Encoding for HTML

Including and requiring files

Modifying headers

Page redirection

Output buffering

Working with Forms and Form Data

Building forms

Detecting form submissions

Single-page form processing

Validating form values

Problems with validation logic

Displaying validation errors

Custom validation functions

Single-page form with validations

Working with Cookies and Sessions

Working with cookies

Setting cookie values

Reading cookie values

Unsetting cookie values

Working with sessions

MySQL Basics

MySQL introduction

Creating a database

Creating a database table

CRUD in MySQL

Populating a MySQL database

Relational database tables

Populating the relational table

Using PHP to Access MySQL

Database APIs in PHP

Connecting to MySQL with PHP

Retrieving data from MySQL

Working with retrieved data

Creating records with PHP

Updating and deleting records with PHP

SQL injection

Escaping strings for MySQL

Introducing prepared statements

Building a Content Management System (CMS)

Blueprinting the application

Building the CMS database

Establishing your work area

Creating and styling the first page

Making page assets reusable

Connecting the application to the database

Using Site Navigation to Choose Content

Adding pages to the navigation subjects

Refactoring the navigation

Selecting pages from the navigation

Highlighting the current page

Moving the navigation to a function

Application CRUD

Finding a subject in the database

Refactoring the page selection

Creating a new subject form

Processing form values and adding subjects

Passing data in the session

Validating form values

Creating an edit subject form

Using single-page submission

Deleting a subject

Cleaning up

Assignment: Pages CRUD

Assignment results: Pages CRUD

Building the Public Area

The public appearance

Using a context for conditional code

Adding a default subject behaviour

The public content area

Protecting page visibility

Regulating Page Access

User authentication overview

Admin CRUD

Encrypting passwords

Salting passwords

Adding password encryption to CMS

New PHP password functions

Creating a login system

Checking for authorization

Creating a logout page

Advanced PHP Techniques

Using variable variables

Applying more array functions

Building dates and times: Epoch/Unix

Formatting dates and times: Strings and SQL

Setting server and request variables

Establishing global and static variable scope

Making a reference assignment

Using references as function arguments

Using references as function return values

Introduction to Object-Oriented Programming (OOP)

Introducing the concept and basics of OOP

Defining classes

Defining class methods

Instantiating a class

Referencing an instance

Defining class properties

OOP in Practice

Understanding class inheritance

Setting access modifiers

Using setters and getters

Working with the static modifier

Reviewing the scope resolution operator

Referencing the Parent class

Using constructors and destructors

Cloning objects

Comparing objects

Working with Files and Directories

File system basics

Understanding file permissions

Setting file permissions

PHP permissions

Accessing files

Writing to files

Deleting files

Moving the file pointer

Reading files

Examining file details

Working with directories

Viewing directory content

Sending Emails

Configuring PHP for email

Sending email with mail()

Using headers

Reviewing SMTP

Using PHPMailer

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Data Science training course in Erode with Python

Data Science training course in Erode with Python

Learn to use Python as your Data Science tool of choice This course teaches you Python as a tool for data science, and specifically for implementing an advanced Machine Learning algorithm with Python

I. Introduction and Setting Up Your Integrated Analysis Environment

Setting Up Your Integrated Analysis Environment & Tools Overview

IPython Shell

Custom environment settings

Jupyter Notebooks

Script editor

Packages: NumPy, SciPy, scikit-learn, Pandas, Matplotlib, Seaborn, etc.

Once you complete this module, you will understand some of the unique benefits of using Python for data science / what features make Python particularly well-suited for data science, you will be able to set up a fully functioning Python-based analysis environment, and you will know what each tool is used for in the data science workflow.

II. Using Python to Control and Document Your Data Science Processes

Python Essentials

Data types and objects

Loading packages, namespaces

Reading and writing data

Simple plotting

Control flow

Debugging

Code profiling

Once you complete this module, you will be able to use the Python standard library plus Canopy tools to write, run, debug, and profile programs that control your data science processes (which draw on the scientific packages).

III. Accessing and Preparing Data

Data, Data, Everywhere...

Acquiring Data with Python

Loading from CSV files

Accessing SQL databases

Cleansing Data with Python

Stripping out extraneous information

Normalizing data

Formatting data

Once you complete this module, you will know how to load data from common types of data sources, including structured text files and SQL databases. and you will know some of the common tools used in Python to cleanse and prepare your data for analysis.

IV. Numerical Analysis, Data Exploration, and Data Visualization with NumPy Arrays, 
Matplotlib, and Seaborn

NumPy Essentials

The NumPy array

N-dimensional array operations and manipulations

Memory mapped files

Data Visualization

2D plotting with Matplotlib

Advanced data visualization with Seaborn

Once you complete this module, you will understand how to use NumPy arrays for efficient numerical processing and how to use NumPy methods such as slicing to write code that is both compact and easy to read and understand. You will know how to use Matplotlib, Seaborn, and NumPy together to explore and visualize your data.

V. Exploring Data with Pandas

Searching for Gold in a Pile of Pyrite

Data manipulation with Pandas

Statistical analysis with Pandas

Time series analysis with Pandas

At the end of this module, you will know how to access some of the core tools used for statistical analysis and data exploration in Python.

VI. Machine Learning with scikit-learn

Predicting the Future Can Be Good for Business

Input: 2D, samples, and features

Estimator, predictor, transformer interfaces

Pre-processing data

Regression

Classification

Model selection

At the end of this module you will have a working understanding of what machine learning tools are available in scikit-learn and how to use them.

Trainer

K.BoopathiKumar

919698548633

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